Distinctive features of persuasion and deliberation dialogues
Why this work is in the frame
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Bibliographic record
Abstract
The distinction between action persuasion dialogues and deliberation dialogues is not always obvious at first sight. In this paper, we provide a characterisation of both types of dialogues that draws out the distinctive features of each. It is important to recognise the distinctions since participants in both types of dialogues will have different aims, which in turn affects whether a successful outcome can be reached. Such dialogues are typically conducted by exchanging arguments for and against certain options. The moves of the dialogue are designed to facilitate such exchanges. In particular, we show how the pre- and post-conditions for the use of particular moves in the dialogues are very different depending upon whether they are used as part of a persuasion over action or a deliberation dialogue. We draw out the distinctions with reference to a running example that we also present as a logic program in order to give a clear characterisation of the two types of dialogues, which is intended to enable them to be used more effectively within systems requiring automated communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it